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@ongkiii
ongkiii / IPA-Sources.md
Last active September 29, 2026 06:00
REPOS/TELEGRAM CHANNELS LIST BY u/angkitbharadwaj

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@sundowndev
sundowndev / GoogleDorking.md
Last active September 29, 2026 05:47
Google dork cheatsheet

Google dork cheatsheet

Search filters

Filter Description Example
allintext Searches for occurrences of all the keywords given. allintext:"keyword"
intext Searches for the occurrences of keywords all at once or one at a time. intext:"keyword"
inurl Searches for a URL matching one of the keywords. inurl:"keyword"
allinurl Searches for a URL matching all the keywords in the query. allinurl:"keyword"
intitle Searches for occurrences of keywords in title all or one. intitle:"keyword"
@JPersson77
JPersson77 / nVAppAppApp.ps1
Last active September 29, 2026 05:46
nVAppAppApp - workaround NVIDIA DLSS4 whitelisting
<# Workaround for NVIDIA's DLSS4 whitelisting
-------- WHAT IS THE BACKSTORY? --------
DLSS4 was launched alongside the RTX 5000 series and comprise several new and interesting
features, f.e. additional presets for Super Resolution, using a newer Transformer model.
Arguably these features increase image quality significantly. To various degrees these
features are also available for older RTX cards, and older games using DLSS3/2.
@antoniolg
antoniolg / jev_server.py
Created September 18, 2026 13:35
Jev Codex Router server with configurable Luna policy and adaptive effort
#!/usr/bin/env python3
"""Jev Codex Router — local server on 127.0.0.1:4319 for the Codex Router.
Receives Responses requests destined for the "jev/auto" model (the Codex
Router's "jev" generic provider), asks Jev (TypeSafe System One) for a tier
and a thinking depth, applies the routing policy, then relays to the Codex
Router's local caller edge (native session sharing enabled) — with no format
conversion: Responses in, Responses out, SSE relayed verbatim.
Default routing policy:
@gadzhimari
gadzhimari / adobe_cc.md
Created November 22, 2018 11:29
Completely Remove Adobe from your Mac in 2 Steps

Step 1

Download and run the Adobe Creative Cloud Cleaner Tool, their multi-app uninstaller and wipe assistant. Adobe does recommend running individual application uninstallers first, your call. Click the Clean All option.

Step 2

Type a one line command in terminal find ~/ -iname "*adobe*" and it's shows up all files which match pattern.

To remove all files

`sudo rm -rf /Applications/Adobe* /Applications/Utilities/Adobe* /Library/Application\ Support/Adobe /Library/Preferences/com.adobe.* /Library/PrivilegedHelperTools/com.adobe.* /private/var/db/receipts/com.adobe.* ~/Library/Application\ Support/Adobe* ~/Library/Application\ Support/com.apple.sharedfilelist/com.apple.LSSharedFileList.ApplicationRecentDocuments/com.adobe* ~/Library/Application\ Support/CrashReporter/Adobe* ~/Library/Caches/Adobe ~/Library/Caches/com.Adobe.* ~/Library/Caches/com.adobe.* ~/Library/Cookies/com.adobe.* ~/Library/Logs/Adobe* ~/Librar

@k16shikano
k16shikano / SKILL.md
Last active September 29, 2026 05:28
cognitive-rhythm-writing/SKILL.md
name cognitive-rhythm-writing
description 説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替(観察→逡巡→断定→再観察)と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。

認知リズムを生むための日本語ライティング規範

密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード(観察する、迷う、確信する、確かめ直す)を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。

@k16shikano
k16shikano / SKILL.md
Last active September 29, 2026 05:12
japanese-tech-writing/SKILL
name japanese-tech-writing
description 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止(「運ぶ」「効く」「開かれた問い」など)、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。
license Unlicense(https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063)

日本語技術文書の文章規範

日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。